Accurate, precise, and timely estimation of crop yield is key to a grower’s ability to proactively manage crop growth and predict harvest logistics. Such yield predictions typically are based on multi-parametric models and in-situ sampling. Here we investigate the extension of a greenhouse study, to low-altitude unmanned aerial systems (UAS). Our principal objective was to investigate snap bean crop (Phaseolus vulgaris) yield using imaging spectroscopy (hyperspectral imaging) in the visible to near-infrared (VNIR; 400–1000 nm) region via UAS. We aimed to solve the problem of crop yield modelling by identifying spectral features explaining yield and evaluating the best time period for accurate yield prediction, early in time. We introduced a Python library, named Jostar, for spectral feature selection. Embedded in Jostar, we proposed a new ranking method for selected features that reaches an agreement between multiple optimization models. Moreover, we implemented a well-known denoising algorithm for the spectral data used in this study. This study benefited from two years of remotely sensed data, captured at multiple instances over the summers of 2019 and 2020, with 24 plots and 18 plots, respectively. Two harvest stage models, early and late harvest, were assessed at two different locations in upstate New York, USA. Six varieties of snap bean were quantified using two components of yield, pod weight and seed length. We used two different vegetation detection algorithms. the Red-Edge Normalized Difference Vegetation Index (RENDVI) and Spectral Angle Mapper (SAM), to subset the fields into vegetation vs. non-vegetation pixels. Partial least squares regression (PLSR) was used as the regression model. Among nine different optimization models embedded in Jostar, we selected the Genetic Algorithm (GA), Ant Colony Optimization (ACO), Simulated Annealing (SA), and Particle Swarm Optimization (PSO) and their resulting joint ranking. The findings show that pod weight can be explained with a high coefficient of determination ($R^2$ = 0.78–0.93) and low root-mean-square error ($RMSE$ = 940–1369 kg/ha) for two years of data. Seed length yield assessment resulted in higher accuracies ($R^2$ = 0.83–0.98) and lower errors ($RMSE$ = 4.245–6.018 mm). Among optimization models used, ACO and SA outperformed others and the SAM vegetation detection approach showed improved results when compared to the RENDVI approach when dense canopies were being examined. Wavelengths at 450, 500, 520, 650, 700, and 760 nm, were identified in almost all data sets and harvest stage models used. The period between 44–55 days after planting (DAP) the optimal time period for yield assessment. Future work should involve transferring the learned concepts to a multispectral system, for eventual operational use; further attention should also be paid to seed length as a ground truth data collection technique, since this yield indicator is far more rapid and straightforward.

The agricultural industry suffers from a significant amount of food waste, some of which originates from an inability to apply site-specific management at the farm-level. Snap bean, a broad-acre crop that covers hundreds of thousands of acres across the USA, is not exempt from this need for informed, within-field, and spatially-explicit management approaches. This study aimed to assess the utility of machine learning algorithms for growth stage and pod maturity classification of snap bean (cv. Huntington), as well as detecting and discriminating spectral and biophysical features that lead to accurate classification results. Four major growth stages and six main sieve size pod maturity levels were evaluated for growth stage and pod maturity classification, respectively. A point-based in situ spectroradiometer in the visible-near-infrared and shortwave-infrared domains (VNIR-SWIR; 400–2500 nm) was used and the radiance values were converted to reflectance to normalize for any illumination change between samples. After preprocessing the raw data, we approached pod maturity assessment with multi-class classification and growth stage determination with binary and multi-class classification methods. Results from the growth stage assessment via the binary method exhibited accuracies ranging from 90–98%, with the best mathematical enhancement method being the continuum-removal approach. The growth stage multi-class classification method used raw reflectance data and identified a pair of wavelengths, 493 nm and 640 nm, in two basic transforms (ratio and normalized difference), yielding high accuracies (~79%). Pod maturity assessment detected narrow-band wavelengths in the VIS and SWIR region, separating between not ready-to-harvest and ready-to-harvest scenarios with classification measures at the ~78% level by using continuum-removed spectra. Our work is a best-case scenario, i.e., we consider it a stepping-stone to understanding snap bean harvest maturity assessment via hyperspectral sensing at a scalable level (i.e., airborne systems). Future work involves transferring the concepts to unmanned aerial system (UAS) field experiments and validating whether or not a simple multispectral camera, mounted on a UAS, could incorporate < 10 spectral bands to meet the need of both growth stage and pod maturity classification in snap bean production.

New York state is among the largest producers of table beets in the United States, which, by extension, has placed a new focus on precision crop management. For example, an operational unmanned aerial system (UAS)-based yield forecasting tool could prove helpful for the efficient management and harvest scheduling of crops for factory feedstock. The objective of this study was to evaluate the feasibility of predicting the weight of table beet roots from spectral and textural features, obtained from hyperspectral images collected via UAS. We identified specific wavelengths with significant predictive ability, e.g., we down-select >200 wavelengths to those spectral indices sensitive to root yield (weight per unit length). Multivariate linear regression was used, and the accuracy and precision were evaluated at different growth stages throughout the season to evaluate temporal plasticity. Models at each growth stage exhibited similar results (albeit with different wavelength indices), with the LOOCV (leave-one-out cross-validation) R² ranging from 0.85 to 0.90 and RMSE of 10.81–12.93% for the best-performing models in each growth stage. Among visible and NIR spectral regions, the 760–920 nm-wavelength region contained the most wavelength indices highly correlated with table beet root yield. We recommend future studies to further test our proposed wavelength indices on data collected from different geographic locations and seasons to validate our results.

Timely assessment of crop maturity contributes to optimized harvesting schedules while limiting food loss/waste at the farm level. Maturity assessments are typically performed via costly and time-consuming in situ methods. This study aimed to evaluate pod size crop maturity using imaging spectroscopy via unmanned aerial systems (UASs), as well as identifying discriminating wavelengths, using snap bean as a proxy crop. The research utilized a UAS-mounted hyperspectral imager in the visible-to-near-infrared region. Two years’ worth of data were collected at two different geographical locations for six different snap bean cultivars. Our approach consisted of calibration to reflectance, vegetation detection, noise reduction, creating classification bins, and feature selection. We used our previously published feature selection library, Jostar, and utilized ant colony optimization and simulated annealing to detect five spectral features and Plus-L Minus-R to identify one to ten features. We utilized decision trees and random forest classifiers for the classification task. Our findings show that, given the proper wavelengths, accurate pod maturity assessment is feasible for large-sieve cultivars (F1 score = 0.79–0.91), separating sieve sizes between ready-to-harvest and not ready-to-harvest pods. These spectral features were in the ~450, ~530, ~660, 700–720, ~740, and ~760 nm regions. This bodes well for the potential extension of results to an operational, multispectral sensor, tuned with the identified bands, thereby negating the need for a costly hyperspectral system. However, this proposition mandates further investigation, including data acquisition from geographical locations with variable climates, and quantifying noise for the hyperspectral imager to compare results with noisier datasets.

White mold caused by Sclerotinia sclerotiorum is a serious disease affecting many field and specialty crops in New York (NY). The primary inoculum for white mold is sclerotia, which are hardened masses of mycelia that survive adverse environmental conditions and periods of nonhosts. However, NY crop guidelines lack rotation and residue management recommendations based on local knowledge of sclerotial survival. A field trial was established in October 2020 by deploying S. sclerotiorum sclerotia in mesh bags on the soil surface or shallowly buried (placed at a 3-cm depth in the soil) at Geneva, NY. Bags were periodically collected from 67 to 769 days. At each collection, sclerotial retrieval (number of sclerotia) was assessed by counting, and their viability was evaluated through myceliogenic germination. Sclerotial retrieval was significantly affected by soil depth and was higher in those on the surface than those buried. Time also affected the retrieval of sclerotia, which was significantly reduced after 250 days. The interaction between burial and time had a significant effect on sclerotial viability. Approximately 15% of sclerotia placed on the surface were still viable after 769 days. After 433 days, the viability of buried sclerotia was also significantly reduced compared to those on the surface. After 670 days, none of the buried sclerotia were viable. These findings suggest a rotation of at least 2 years between susceptible crops is required to reduce primary inoculum. However, given that low inoculum densities are sufficient to initiate a white mold outbreak, a longer rotation may be beneficial. In a cultivated system, timely tillage of crop residue to bury sclerotia after harvest to promote degradation is encouraged.

Pale swallowwort [Vincetoxicum rossicum (Kleopow) Barbar.; syn.: Cynanchum rossicum (Kleopow) Borhidi] and black swallowwort [Vincetoxicum nigrum (L.) Moench; syn.: Cynanchum louiseae Kartesz & Gandhi] are invasive perennial viny milkweeds that have become prevalent across natural and managed habitats in northeastern North America. Southern blight of V. rossicum caused by the fungus Athelia rolfsii (Curzi) C. C. Tu & Kimbr. was reported at a New York county park in 2008, resulting in a decline in V. rossicum stands. The disease outbreak and persistence of the pathogen highlighted the potential of A. rolfsii for Vincetoxicum spp. control. To better characterize A. rolfsii’s pathogenicity and biology, we studied virulence to adult Vincetoxicum spp., spatiotemporal attributes of the Southern blight epidemic at the discovery site over 4 yr, and sclerotial survival over 2 yr. Disease incidence and severity were high for both Vincetoxicum spp. in misting chamber experiments. The spatiotemporal spread patterns of Southern blight in V. rossicum suggest the epidemic in the first year of monitoring (2016) was already highly aggregated and that subsequent spread was limited and resulted in significant local aggregation. Sclerotial survival studies at two locations (Pittsford and Ithaca, NY) demonstrated the A. rolfsii isolates can overwinter in upstate New York and are pathogenic to Vincetoxicum spp. the subsequent season. However, shallow burial of sclerotia more rapidly reduced survival compared with placement on the soil surface. Overwinter survival of A. rolfsii sclerotia in New York is notable, as this pathogen is typically associated with subtropical and tropical regions. Broadcast applications of the pathogen would be needed for widespread Vincetoxicum control at a site, but even restricting releases to select locations would not prevent pathogen movement off-site via water or machinery. The known risks of the A. rolfsii isolate to other broadleaf plants in natural and agricultural settings suggest a low feasibility of use for the biological control of Vincetoxicum spp.